Novel two-stage algorithm for non-parametric cast shadow recognition.
Intelligent Vehicles Symposium(2011)
摘要
Environment perception and scene understanding is an important issue for modern driver assistance systems. However, adverse weather situations and disadvantageous illumination conditions like cast shadows have a negative effect on the proper operation of these systems.In this paper, we propose a novel approach for cast shadow recognition in monoscopic color images. In a first step, shadow edge candidates are extracted evaluating binarized channels in the color-opponent and perceptually uniform CIE L*a*b* space. False detections are rejected in a second verification step, using SVM classification and a combination of meaningful color features. We introduce a non-parametric representation for complex shadow edge geometries that enables utilizing shadow edge information for improving downstream vision-based driver assistance systems. A quantitative evaluation of the classification performance as well as results on multiple real-world traffic scenes show a reliable cast shadow recognition with only a few false detections.
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关键词
image colour analysis,support vector machines,traffic engineering computing,SVM classification,complex shadow edge geometries,downstream vision-based driver assistance systems,environment perception,monoscopic color images,nonparametric cast shadow recognition,nonparametric representation,scene understanding,shadow edge information,two-stage algorithm
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